scholarly journals Densification of (GNSS) Control Points for Cadastral and Mapping Purposes

2017 ◽  
Vol 1 (2) ◽  
pp. 287-298 ◽  
Author(s):  
M. O. Ehigiator ◽  
S. O. Oladosu ◽  
R. Ehigiator-Irughe
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Control Points ◽  
Network Adjustment ◽  
Static Mode ◽  
Continuous Exercise ◽  
Master Station

GNSS control densification is a continuous exercise in the field of Geomatics. This form the basis upon which other Geomatics and Engineering activities geared toward development are referenced. This paper employed the use of Hi-Target GPS to extend and establish controls at the confines of the study area in static mode while topographical survey was carried out using real-time kinematics method. Network adjustment, for the newly established control stations were carried out while the master station was held fixed. Data analysis and production of plans were done using softwares like Hi-Target V30, Carlson Civil Suite 2017 etc. The result of perimeter computation for the study area gave a total of 93.614 hectare

User-centered Requirements for Patient Fluid Intake Monitoring Support

2020 ◽  
Vol 64 (1) ◽  
pp. 736-740
Author(s):  
Ellen J. Bass ◽  
Andrew J. Abbate ◽  
Yaman Noaiseh ◽  
Rose Ann DiMaria-Ghalili
Keyword(s):  
Data Analysis ◽  
Focus Group ◽  
Focus Groups ◽  
Real Time ◽  
Information Needs ◽  
Fluid Intake ◽  
Fluid Consumption ◽  
Registered Dietitians ◽  
Group Data ◽  
Focus Group Data

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis.

scholarly journals Real-time data analysis for medical diagnosis using FPGA-accelerated neural networks

2018 ◽  
Vol 19 (S18) ◽  
Author(s):  
Ahmed Sanaullah ◽  
Chen Yang ◽  
Yuri Alexeev ◽  
Kazutomo Yoshii ◽  
Martin C. Herbordt
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Real Time ◽  
Medical Diagnosis ◽  
Time Data ◽  
Real Time Data

scholarly journals A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Real Time ◽  
Semiconductor Manufacturing ◽  
Reference Model ◽  
Big Data Analysis ◽  
Monitoring Method ◽  
Manufacturing Equipment

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

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